How Behaviours on Digitalised Social-Media Platforms Facilitates Identity Theft

  • Omobolaji Epoyun

Student thesis: Master's Thesis


This research investigates how the attitudes and behaviours of users of digitalised social media platforms (DSPs) enable identity theft and if DSPs inadvertently create the enabling environment for identity theft. The study investigates how such factors as the possession of DSPs, trust of DSPs to secure personal data, and awareness contribute to identity theft. The study also employed a mixed-methods sequential approach combining quantitative and qualitative methods and utilises Routine Activity theory (REF) to explore the conjunction of an offender, victim and situational factors that may create opportunities for identity theft in DSPs. The key findings of this research encompass the following four areas; first, DSP users' attitudes on these platforms increase their susceptibility to identity theft and related crimes. Second, their excessive exposure to the platforms encourages them to lose their sense of security gradually and increases their chances of sharing sensitive personal information more often than not. Third, their behaviour of posting excessive private information also sometimes exposes them to crime risks beyond DSPs, including physical assault. Finally, the study reveals that many users do not perceive them as trustworthy in securing their information and upholding privacy. However, this contradicts the earlier findings showing that most users post excessive personally identifiable information on social media. It seems abnormal to tolerate information vulnerabilities from digitalised platforms when the user is not expecting safety from them. The study contributes to the development of theory by extending the application of the Routine Activity Theory (RAT) to DSP while adding more specificity to the different approaches through which users of DSP encourage identity theft and better understand the criminal mechanism of identity theft in DSPs.
Date of Award17 Apr 2023
Original languageEnglish
SupervisorMichelle Rogerson (Main Supervisor)

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